Chemokine receptors(CRs)have long been druggable targets for the treatment of inflammatory diseases and HIV-1 infection.As a powerful technique,virtual screening(VS)has been widely applied to identifying small molecule leads for modern drug targets including CRs.For rational selection of a wide variety of VS approaches,ligand enrichment assessment based on a benchmarking data set has become an indispensable practice.However,the lack of versatile benchmarking sets for the whole CRs family that are able to unbiasedly evaluate every single approaches including both structure-and ligand-based VS,somewhat hinders modern drug discovery efforts.To address this issue,we constructed Maximal-Unbiased Benchmarking Data sets for human Chemokine Receptors(MUBD-hCRs)using our recently developed tools of MUBD-DecoyMaker.The MUBD-hCRs encompasses 13 subtypes out of 20 chemokine receptors,composed of 404 ligands and 15756 decoys so far and are readily expandable in the future.It had been thoroughly validated that MUBD-hCRs ligands are chemically diverse while its decoys are maximal-unbiased in terms of “artificial enrichment”,“analogue bias”.In addition,we studied the performance of MUBD-hCRs,in particular CXCR4 and CCR5 data sets,in ligand enrichment assessments of both structure and ligand-based VS approaches in comparison with other benchmarking data sets available in public domain and demonstrated that MUBD-hCRs is much capable of designating the optimal VS approach.Taken together,MUBD-hCRs is a unique and maximal-unbiased benchmarking set that covers major CRs subtypes so far.